1. Field of the Invention
The present invention relates to a method of modeling user preference, and more particularly, to a user preference modeling method using fuzzy networks.
2. Description of the Related Art
New trends in software are geared toward how to efficiently serve specific information a user wants to access. To this end, user preference must be individualized and it is important to cope with such user preference. That is, a computer system acquires user information through communications with the user and infers to get more information, thereby providing good information which reflects the user""s taste.
The type of information stored in a computer for this purpose is called a xe2x80x9cuser modelxe2x80x9d and the processes for acquiring information from the user and inferring more information therefrom are called xe2x80x9cuser modelingxe2x80x9d.
The problem in grasping the users taste always implies some degree of uncertainty because inferring on the user""s taste using a computer cannot ensure 100% certainty. Thus, user modeling capable of exposing such inevitable uncertainty and solving the problem of uncertainty is required.
For user modeling, the Bayesian network method, the Dempster-Shafer evidence theory and a method based on the fuzzy theory have been introduced.
Firstly, the Bayesian network method is based on the Bayesian probability theory and is expressed as a graph illustrating the relationship between each parameter extracted to model the user preference. Then, probability values of each parameter are corrected using information input from the outside, and the overall user preference is inferred using the inference algorithm based on the relationship among each parameter.
Secondly, the Dempster-Shafer evidence theory expresses the uncertainty as a concept of interval as opposed to the probability theory which adopts a figure to express the uncertainty. Also, the degree of effect of the information provided from the outside on each individual taste of the user is calculated and then generalized to grasp the overall user preference.
Thirdly, the method based on the fuzzy theory applies all expression and inferring activities occurring in daily life to a computer, which is very useful in managing uncertainty in user modeling.
The above methods have been used to model knowledge, goals, experience and background of the user. However, user preference rather than other information is easily changeable, so it is difficult to generalize all information required for the user modeling. Thus, there is a problem in modeling the user preference using the conventional methods.
It is an object of the present invention to provide a user preference modeling method using fuzzy networks, capable of easily modeling a user preference.
It is another object of the present invention to provide a method of serving an adaptive web directory using fuzzy networks, capable of restructuring a web directory structure according to the user preference using a user preference modeling method based on the fuzzy networks.
It is still another object of the present invention to provide a computer readable medium storing a computer program for the user preference modeling using fuzzy networks.
To achieve the first object of the present invention, there is provided a user preference modeling method using fuzzy networks, comprising the steps of: (a) changing a user modeling structure into a fuzzy network structure in which a plurality of layers including one or more graphs with one or more nodes, are stacked; (b) when information is input from a user, searching a node directly associated with the input information on the fuzzy networks, and calculating a new preference for the node with a predetermined equation; (c) calculating connection strengths among each node in a graph to which the node belongs according to the new preference obtained in step (b) and calculating a new preference for each node of the graph according to the connection strengths; (d) when the node of the graph to which the node searched in step (b) belongs is a macro node of a graph of a lower layer, and a node is defined as the macro node if a graph of a lower layer defines sub-regions of the node, transferring a first message as preference change information from the macro node to the graph of the lower layer; (e) when the graph to which the node searched in step (b) belongs has a macro node in an upper layer, transferring a second message to the macro node, as preference change information for all nodes of the graph to which the node belongs; (f) when a graph receives the first message from a macro node, calculating a new preference for all nodes in the graph that has received the first message, and when a node of the graph that has received the first message is a macro node of a graph of a lower layer, transferring a first message as preference change information to the graph of the lower layer; and (g) when a node receives the second message from a graph of a lower layer, calculating a new preference for the node that has received the second message and performing step (c) through (e) to other nodes.
To achieve the second object of the present invention, there is provided a method of serving an adaptive web directory using fuzzy networks, comprising the steps of: (a) changing a web directory structure on a web server into a fuzzy network structure in which a plurality of layers including one or more graphs with one or more nodes, are stacked; (b) when information is input by a user, searching a node on the fuzzy networks, the node being directly associated with the input information, and calculating a new preference for the node; (c) calculating connection strengths for all nodes of a graph to which the node searched in step (b) belongs, based on the new preference obtained in step (b), and calculating new preferences for each node of the graph in consideration of the connection strengths; (d) when a node of the graph to which the node searched in step (b) belongs is a macro node of a graph of a lower layer, and a node is defined as the macro node if a graph of a lower layer defines sub-regions of the node, transferring a first message as preference change information from the macro node to the graph of the lower layer; (e) when the graph to which the node searched in step (b) belongs has a macro node in an upper layer, transferring a second message to the macro node, as change information for all nodes of the graph to which the node belongs; (f) when a graph receives the first message from a macro node, calculating a new preference for all nodes in the graph that has received the first message, and when a node of the graph that has received the first message is a macro node of a graph of a lower layer, transferring a first message as preference change information to the graph of the lower layer; (g) when a node receives the second message from a graph of a lower layer, calculating a new preference for the node that has received the second message and performing step (c) through (a) to other nodes: and (h) restructuring the web directory according to the user preference to provide a web directory structure adaptable to the user preference characteristic.
The invention may be embodied in a general purpose digital computer that is running a program from a computer usable medium, including but not limited to storage media such as magnetic storage media (e.g., ROM""s, floppy disks, hard disks, etc.), optically readable media (e.g., CD-ROMs, DVDs, etc.) and carrier waves (e.g., transmissions over the Internet). Hence, the present invention may be embodied as a computer usable medium.
According to still another object of the present invention, there is provided a computer readable medium storing a computer program for the user preference modeling method using fuzzy networks, wherein the user preference modeling comprises the steps of: (a) changing a user modeling structure into a fuzzy network structure in which a plurality of layers including one or more graphs with one or more nodes, are stacked; (b) when information is input from a user, searching a node directly associated with the input information on the fuzzy networks, and calculating a new preference for the node with a predetermined equation; (c) calculating connection strengths among each node in a graph to which the node belongs according to the new preference obtained in step (b) and calculating a new preference for each node of the graph according to the connection strengths; (d) when the node of the graph to which the node searched in step (b) belongs is a macro node of a graph of a lower layer, and a node is defined as the macro node if a graph of a lower layer defines sub-regions of the node, transferring a first message as preference change information from the macro node to the graph of the lower layer; (e) when the graph to which the node searched in step (b) belongs has a macro node in an upper layer, transferring a second message to the macro node, as change information for all nodes of the graph to which the node belongs; (f) when a graph receives the first message from a macro node, calculating a new preference for all nodes in the graph that has received the first message, and when the node of the graph that has received the first message is a macro node of a graph of a lower layer, transferring a first message as preference change information to the graph of the lower layer; and (g) when a node receives the second message from a graph of a lower layer, calculating a new preference for the node that has received the second message and performing step (c) through (e) to other nodes.